The primary objective of this dissertation was to investigate the effects of varying
levels of luminance, color, and spatial frequency content on the perceived image quality of
a soft-copy color image. A secondary objective was to test the robustness of selected
image quality metrics (MTFA, SQRI, and ICS) to the color variations as measured by the
change in correlations between the perceived quality ratings and the values of the image
quality metrics. To accomplish these objectives, a color image was selected and its
luminance, color, and spatial frequency components were attenuated systematically using
image processing software. With the manipulated images, an experiment was conducted in
which subjects were asked to rate, on a 0.0 - 9.0 continuous scale, the perceived quality of
a displayed image in comparison to the original image. Results of the statistical analysis of
the collected data were characterized by the highly significant main effects and interaction
effects. However, the magnitudes of the interactions were small.

The effect of the luminance component on perceived quality was found to be
dominant and consistent across all the levels of the other two variables. As the luminance
increased, the perceived quality increased at a decreasing rate. The luminance main effect
was modeled well (R2 = 0.9968) by the second-order polynomial of the luminance
attenuation level, or, equivalently, by the relative amount of the luminance contained in the
image. The range of variation of perceived quality produced by the six luminance levels
was about five units on a 0.0 - 9.0 continuous scale. It was concluded that perceived
quality of the color image was determined primarily by the luminance component of the
image.

The effect of color on perceived quality was found to be smaller than expected. The
range of variation in perceived quality produced by the six color levels was only a little over
one unit on a 0.0 - 9.0 continuous scale. Perceived qualities increased at a decreasing rate
as the level of color increased. However, the slope of the curve representing the color
effect was smaller than that of the luminance effect The main effect of color was modeled
well (R2 = 0.9972) by the second-order polynomial of the color attenuation level, or,
equivalently, by the relative amount of color contained in the image. Based on the findings
of the color effect, two different roles of color in image perception are suggested. At
extremely low luminance, color acts primarily as a facilitator of the luminance by providing
more cues on the content of the image. At sufficiently high luminance, the increased
perceived quality stems from the aesthetic characteristics of the color.

Both highpass and lowpass filtering, on the average, caused about 1.5 units of
degradation as compared to the unfiltered image in perceived image quality on a 0.0 - 9.0
continuous scale. The perceived quality of the unfiltered image was greater than that of the
filtered images across all the levels of luminance and color attenuation except at a low
luminance level. There was no significant difference between the perceived qualities of the
highpass and lowpass filtered images.

The R2 of the second-order polynomial for image qUality metrics (MTFA, SQRI,
and ICS) and the mean perceived qualities did not vary across the color variations in the
image manipulations. That is, these image quality metrics were robust to the color
variations when the relationship between the quality metric values and the actual perceived
qualities was represented by the second-order polynomial. However, with the first-order
model, the R2 increased as the color level increased. The SQRI yielded higher R2 values
than did the MTFA and ICS metrics when the first-order model was used. Also, the range
of variation of R2 for the SQRI was smaller than that for the other two metrics. Therefore,
it appears that the robustness of an image quality metric to the color variation is affected by
the degree of non-linearity correction in the metric if the robustness is tested in the context
of the straight-line relationship.